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Check out the documentation for more information.

Vision-OPD Intrinsic-Reward Experiments — Best Checkpoints (ready to eval)

Merged HF-format weights (bf16 safetensors + tokenizer + image processor configs) of the best checkpoint of each of the 6 experiments: {TTRL, Intuitor, RENT} × {Qwen3.5-4B, Qwen3.5-9B} trained on Vision-OPD-6K. Best = highest 6-benchmark mean on the held-out selection split (3216 items, judge Qwen2.5-72B-Instruct). Each folder loads directly with transformers / vLLM.

folder method model recipe best step avg6 (select)
ttrl-4b-nokl-best-s20/ TTRL Qwen3.5-4B no KL 20 74.46
intuitor-4b-nokl-best-s5/ Intuitor Qwen3.5-4B no KL 5 70.25
rent-4b-kl-best-s5/ RENT Qwen3.5-4B KL β=0.01 5 71.03
ttrl-9b-nokl-best-s15/ TTRL Qwen3.5-9B no KL 15 78.46
intuitor-9b-kl-best-s5/ Intuitor Qwen3.5-9B KL β=0.01 5 76.85
rent-9b-kl-best-s5/ RENT Qwen3.5-9B KL β=0.01 5 77.87

Benchmarks: V*Bench, ZoomBench, HR-Bench 4K, HR-Bench 8K, MME-RealWorld, MME-RealWorld-CN.

Usage

from huggingface_hub import snapshot_download
path = snapshot_download("q1716523669/visionopd-best-ckpts",
                         allow_patterns="ttrl-9b-nokl-best-s15/*")
# then point vLLM / transformers at f"{path}/ttrl-9b-nokl-best-s15"

Eval settings used in our record: vLLM, temperature 0.0, seed 0, max_tokens 2048, max-model-len ≥ 32768 (HR-Bench 8K inputs reach >16k tokens).

Full 13-checkpoint FSDP archive (incl. optimizer state), logs and eval outputs: q1716523669/visionopd-archive.

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